Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis

Publication Type:
Journal Article
Citation:
Expert Systems with Applications, 2012, 39 (5), pp. 4947 - 4953
Issue Date:
2012-04-01
Full metadata record
Files in This Item:
Filename Description Size
1-s2.0-S0957417411015077-main.pdfPublished Version311.96 kB
Adobe PDF
In this paper, a relevance vector machine based infinite decision agent ensemble learning (RVM Ideal ) system is proposed for the robust credit risk analysis. In the first level of our model, we adopt soft margin boosting to overcome overfitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron Kernel is employed in RVM to simulate infinite subagents. Our system RVM Ideal also shares some good properties, such as good generalization performance, immunity to overfitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy. © 2011 Elsevier Ltd. All rights reserved.
Please use this identifier to cite or link to this item: